DocDiff: Document Enhancement via Residual Diffusion Models (2305.03892v2)

Abstract: Removing degradation from document images not only improves their visual quality and readability, but also enhances the performance of numerous automated document analysis and recognition tasks. However, existing regression-based methods optimized for pixel-level distortion reduction tend to suffer from significant loss of high-frequency information, leading to distorted and blurred text edges. To compensate for this major deficiency, we propose DocDiff, the first diffusion-based framework specifically designed for diverse challenging document enhancement problems, including document deblurring, denoising, and removal of watermarks and seals. DocDiff consists of two modules: the Coarse Predictor (CP), which is responsible for recovering the primary low-frequency content, and the High-Frequency Residual Refinement (HRR) module, which adopts the diffusion models to predict the residual (high-frequency information, including text edges), between the ground-truth and the CP-predicted image. DocDiff is a compact and computationally efficient model that benefits from a well-designed network architecture, an optimized training loss objective, and a deterministic sampling process with short time steps. Extensive experiments demonstrate that DocDiff achieves state-of-the-art (SOTA) performance on multiple benchmark datasets, and can significantly enhance the readability and recognizability of degraded document images. Furthermore, our proposed HRR module in pre-trained DocDiff is plug-and-play and ready-to-use, with only 4.17M parameters. It greatly sharpens the text edges generated by SOTA deblurring methods without additional joint training. Available codes: https://github.com/Royalvice/DocDiff

• The paper introduces a novel residual diffusion framework that effectively restores high-frequency details in degraded document images.
• It employs a dual-module approach with a Coarse Predictor for low-frequency recovery and a High-Frequency Residual Refinement to enhance text edges.
• The method outperforms existing GAN and CNN approaches by achieving superior perceptual quality with only 8.20M parameters and efficient inference.

An Expert Overview of "DocDiff: Document Enhancement via Residual Diffusion Models"

The paper "DocDiff: Document Enhancement via Residual Diffusion Models," presented at the prestigious 31st ACM International Conference on Multimedia, introduces an innovative approach to enhancing degraded document images using diffusion models. This work seeks to address prevalent challenges in document image processing, particularly the loss of high-frequency information often encountered in traditional regression-based solutions. Through the development of DocDiff, the authors leverage residual diffusion models to manage complicated document enhancement tasks, such as deblurring, denoising, and watermark removal. This essay provides an assessment of the paper, emphasizing its methodologies, results, and potential implications for future research in document image processing.

Methodology

This work is motivated by the limited capabilities of existing pixel-level regression methods that typically produce blurred text artifacts in document images due to a loss of high-frequency information. DocDiff is designed as a diffusion-based framework, which sets itself apart with a focus on conditional image generation, split into two primary modules: the Coarse Predictor (CP) and the High-Frequency Residual Refinement (HRR) module.

1. Coarse Predictor (CP): The CP is responsible for recovering the low-frequency content of the degraded image, functioning as a baseline restoration network.
2. High-Frequency Residual Refinement (HRR): The HRR uses a diffusion process adapted to document image characteristics. It generates high-frequency details such as text edges by estimating the residuals between the ground-truth and CP outputs. This is crucial for maintaining text readability and recognizability.

An essential aspect of this methodology is the deterministic short-step sampling strategy during inference, which facilitates efficient restoration of document images without sacrificing sharpness. The authors adopt a loss function that integrates both pixel-level and diffusion model-based losses, optimizing the model's performance for document-specific tasks.

Experimental Results

The empirical evaluations of DocDiff highlight its superior performance over existing methods across a range of document enhancement tasks, validated using benchmark datasets such as the Document Deblurring dataset and various (H-)DIBCO competitions. Notably, the HRR module demonstrates considerable improvement in perceptual quality metrics, including MANIQA and LPIPS, even with fewer than 100 sampling steps.

The paper provides both quantitative and qualitative comparative analyses, showing that DocDiff not only surpasses traditional GAN-based and CNN-based methods in maintaining the edge details of text but also achieves this with a reduced computational burden. With only 8.20M parameters—a notably compact architecture—DocDiff achieves competitive or superior performance compared to state-of-the-art methods, demonstrating its efficacy and efficiency.

Additionally, the plug-and-play nature of the HRR module allows seamless integration with other deblurring methods, further enhancing text sharpness without additional training. This attribute underscores the versatility and scalability of DocDiff.

Implications and Future Directions

By pushing the boundaries of document enhancement with a diffusion model approach, this research opens avenues for more robust and nuanced document image processing systems. DocDiff's success can be attributed to its ability to distinctly separate and handle high and low-frequency information, an area that warrants further exploration for other image enhancement applications. Future research might also consider integrating text recognition engines to fine-tune enhancements based on OCR feedback, potentially creating a multi-model framework for document restoration.

Overall, this research effectively addresses a critical deficiency in document image processing, setting the stage for continued advancements in both theoretical research and practical applications in AI-powered document analysis systems. As the field evolves, further optimizations in terms of dataset diversity, model architecture, and real-time processing capabilities are promising directions to be explored.